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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi
Dian Saditri, Winny
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Computer Science & IT Education Other

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Predicting the Accuracy of Non-Cash Food Assistance Program in Aceh Using Logistic Regression Biner in Aceh Province: How is the Condition? Dian Safitri, Winny; Dian Saditri, Winny; Azzahra, Fina; Hakim, Rajul; Radha Novarianti , Siti
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33969

Abstract

This study examines the effectiveness of the Non-Cash Food Assistance Program (BPNT) in alleviating poverty in Aceh Province, the poorest province on the island of Sumatra. The research utilizes data from the 2022 National Socioeconomic Survey (Susenas) to analyze household characteristics and determine factors influencing BPNT eligibility. Binary logistic regression and data balancing with SMOTE were applied to assess classification accuracy. Results indicate that households without adequate basic amenities, such as proper toilets and electricity, and those with limited access to resources, such as well water, firewood for cooking, and lack of household assets, are more likely to qualify for BPNT. The logistic regression model achieved an accuracy of 80.83%, with high recall for "Recipient" classification. This study findings highlight is that economic hardship, household size, and physical conditions are significant determinants of BPNT eligibility. This study suggests that targeted assistance for poverty alleviation can be optimized through refined eligibility criteria and data accuracy improvements.
Co-Authors Azzahra, Fina HAKIM, Rajul Radha Novarianti , Siti Winny Dian Safitri